首页|Studies from Tanta University Reveal New Findings on Machine Learning (Productiv ity Prediction of a Spherical Distiller Using a Machine Learning Model and Trian gulation Topology Aggregation Optimizer)
Studies from Tanta University Reveal New Findings on Machine Learning (Productiv ity Prediction of a Spherical Distiller Using a Machine Learning Model and Trian gulation Topology Aggregation Optimizer)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting originating from Tanta, Egypt, by Ne wsRx correspondents, research stated, “Solar stills offer a sustainable and envi ronmentally friendly solution to water scarcity in remote areas, but their limit ed productivity hinders their wider adoption. This study proposes innovative mod ifications to the spherical solar distiller to address this challenge.” Our news editors obtained a quote from the research from Tanta University, “We i ntroduce a rotating spherical ball within the distiller and investigate its impa ct on productivity at various speeds (0-2 rpm) with and without a wick. Addition ally, we explore the effectiveness of preheating feed water to different tempera tures (45-70 degrees C) and its interaction with the rotating ball mechanism. Mo reover, six machine learning models were employed to predict the water productiv ity of the distillers under different working conditions. The employed models we re standalone long short-term memory (LSTM), LSTM optimized by reptile search al gorithm, LSTM optimized by grey wolf optimizer, LSTM optimized by dwarf mongoose optimization algorithm, LSTM optimized by manta ray foraging optimizer, LSTM op timized by triangulation topology aggregation optimizer. The results showcased t hat with an optimal rotation speed of 0.5 rpm and 1 rpm for configurations with and without wick, respectively, we achieved productivity increases of 62 % and 55 %. Notably, preheating feed water to 65 degrees C further bo osted the new distiller performance, surpassing the conventional solar still by 91 %, achieving an impressive output of 6000-6200 mL/m2.day compare d to 3000-3250 mL/m2.day for the conventional distiller. Moreover, the thermal e fficiency of the new distiller configuration reached 62 %, almost d oubling that of the conventional distiller (32 %).”
TantaEgyptAfricaCyborgsEmerging TechnologiesMachine LearningTanta University
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